Creating pattern templates for engine mix settings

ABSTRACT

Race car settings (e.g., Formula 1 engine mix settings) are developed for particular racing goals such as faster lap time, better acceleration, less vehicle wear, etc., using pattern templates that are derived from historical racing scenarios. The historical scenarios provide data on racing settings, racing results, and racing conditions such as squad information, equipment information, and environmental information. A cognitive (deep question answering) system can select an initial pattern template based on current racing conditions, and present suggested vehicle settings to the user (driver) using the initial pattern template. The driver can select from different candidate values for various factors, which may lead to the presentation of additional suggestions or the use of additional pattern templates. The final settings map is created based on the employed pattern templates and the driver selections.

CROSS-REFERENCE TO RELATED APPLICATION

This application is related to U.S. patent application Ser. No. ______entitled “USING COGNITIVE ANALYSIS WITH PATTERN TEMPLATES TO COMPOSEENGINE MAPPING MIX SETTINGS” (attorney docket no. AUS920160433US1) filedconcurrently herewith, which is hereby incorporated.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention generally relates to setting operationalparameters for a vehicle, and more particularly to a method ofcalculating automotive settings in a high performance race car.

Description of the Related Art

Modern Formula 1 and other high performance race cars use complex enginemanagement strategies to optimize performance and reliability of thecars on race tracks. High performance race cars use engine managementsoftware that modifies the mix and other automotive settings ofelectronic control unit that control the power unit and other componentsof the vehicle. The mix settings engine mode and other automotivesettings can be adjusted by the driver from the cockpit using dials andswitches on the steering wheel. These settings are adjusted based onrace conditions, weather, and many other factors. A limited number ofsettings are available to the driver and race rules require that thesemust be programmed into the engine management systems before qualifying.The availability and selection of settings for specific situations,conditions or corners can significantly affect on-track performance andrace results.

Engine mix settings are detail changes to fueling that are routinelyused during the race to optimize between fuel consumption and enginepower. This is also known as the “air-fuel ratio” (AFR), which is themass ratio of air to fuel present in a combustion process such as in aninternal combustion engine. The AFR is an important measure forperformance-tuning reasons. If exactly enough air is provided tocompletely burn all of the fuel, the ratio is known as thestoichiometric mixture. AFR numbers lower than stoichiometric areconsidered “rich”. Rich mixtures are less efficient, but may producemore power and burn cooler, which is kinder on the engine. AFR numbershigher than stoichiometric are considered “lean.” Lean mixtures are moreefficient but may cause engine damage or premature wear and producehigher levels of nitrogen oxides. Other settings which are used tooptimize performance include the car's camber angle, shock absorberstiffness, and front/rear height among others.

In Formula 1, the driver can choose which settings to use during anyparticular stint, segment or corner, but the mix settings map and otherengine maps (e.g., engine torque map) must be designed before the racequalifying and the maps cannot be changed during the race. For example,as the car goes down a straight section of the race track into ahair-pin corner, the driver may change the mix settings to helpaccelerate out of the hair-pin, while a different setting may be usedwhen entering a set of S-curves. Similarly, mix settings may be changedto optimize engine reliability, fuel consumption, tire wear, and evendriver fatigue. Another example would be that when tire wear is detectedthe driver may be instructed by a human advisor to change a certainsetting to a specific value, which then programs the engine performanceto be less demanding on the tires. Automotive settings are also adjustedto improve reliability of parts that may need to be used in futureraces.

The mix settings are configured in a mix settings map (also known as anignition map). The mix settings map is constructed by the team beforethe race begins based on prior experience, and its usage during the racecan greatly influence the lap times, passing opportunities, full-racetimes of the cars, race results and the success of the team. Creatingthe mix settings map is an application of Big Data analytics. Up to 2gigabytes of data can be collected for each car per race just intelemetry, a single race weekend can generate over 100 gigabytes ofdata, and a season generates terabytes of data per car. The software andprocedures the teams use to create mix settings maps is proprietary.Teams likely use spreadsheets, analysis software, database reports, andperformance graphs to analyze the data, determine the strategy for themix settings map, and then produce a map which is then refined throughpractice and testing.

SUMMARY OF THE INVENTION

The present invention in at least one embodiment is generally directedto developing a racing strategy for vehicle settings by receiving aplurality of racing scenarios from a racing data repository wherein eachracing scenario includes racing conditions, racing vehicle settings, andracing results, analyzing the racing scenarios to associate specificracing vehicle settings with certain racing conditions and optimalracing results, and creating a pattern template for one or more racinggoals based on the specific racing vehicle settings and certain racingconditions. The racing goals can be for example faster lap time, betteracceleration, less vehicle wear, better fuel economy, more stability,and improved cornering. Racing conditions can include items such assquad information, equipment information, and environmental information.In some embodiments, the pattern template includes a set of filters, aset of suggestions for the racing vehicle settings, or a factor havingmultiple candidate settings. The settings can for example include anengine mix setting specifying an air-to-fuel ratio.

The above as well as additional objectives, features, and advantages inthe various embodiments of the present invention will become apparent inthe following detailed written description.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features, and advantages of its various embodiments madeapparent to those skilled in the art by referencing the accompanyingdrawings.

FIG. 1 is a block diagram of a computer system programmed to carry outcognitive analysis and/or pattern template generation in accordance withone implementation of the present invention;

FIG. 2 is a block diagram of cognitive analysis logic that may becarried out by the computer system of FIG. 2 to generate patterntemplates for engine mapping mix settings in accordance with oneimplementation of the present invention;

FIG. 3 is a block diagram of a series of exemplary pattern templatesconstructed in accordance with one implementation of the presentinvention;

FIG. 4 is a pictorial representation of a conversational user interfacein accordance with one implementation of the present invention;

FIG. 5 is a pictorial representation of a specific pattern template asdisplayed on a user interface and modified by a user in accordance withone implementation of the present invention;

FIG. 6 is a chart illustrating the logical flow for pattern templategeneration in accordance with one implementation of the presentinvention; and

FIG. 7 is a chart illustrating the logical flow for deriving andpublishing vehicle settings via one or more specific pattern templatesin accordance with one implementation of the present invention.

The use of the same reference symbols in different drawings indicatessimilar or identical items.

DESCRIPTION OF THE PREFERRED EMBODIMENT(S)

The term “artificial intelligence” is applied when a machine mimicscognitive functions that humans associate with other human minds, suchas learning and problem solving. A modern implementation of artificialintelligence is the IBM Watson™ cognitive technology, which is anartificially intelligent computer system capable of answering questionsposed in natural language. IBM Watson is a deep question answering (QA)system which applies advanced natural language processing, informationretrieval, knowledge representation, automated reasoning, and machinelearning technologies to the field of open domain question answering.More than 100 different techniques are used to analyze natural language,identify sources, find and generate hypotheses, find and score evidence,and merge and rank hypotheses. The more algorithms that find the sameanswer independently, the more likely that answer is correct.

As interactions between users and computer systems become more complex,it becomes increasingly important to provide a more intuitive interfacefor a user to issue commands and queries to a computer system. As partof this effort, many systems employ some form of natural languageprocessing. Natural language processing (NLP) is a field of computerscience, artificial intelligence, and linguistics concerned with theinteractions between computers and human (natural) languages. Manychallenges in NLP involve natural language understanding, that is,enabling computers to derive meaning from human or natural languageinput, and others involve natural language generation allowing computersto respond in a manner familiar to a user. For example, a non-technicalperson may input a natural language question to a computer system, andthe system intelligence can provide a natural language answer which theuser can hopefully understand.

Deep question answering systems can identify passages from documents(corpora) and analyze them in various ways in order to extract answersrelevant to a query; answers can be scored on a number of factors, andthe highest score indicates the “best” answer. Models for scoring andranking the answer are trained on the basis of large sets of questionand answer pairs.

One implementation of IBM Watson is the IBM Watson Retrieve and Rank™service, which combines several machine learning techniques (known aslearning-to-rank algorithms) to help users find documents that are morerelevant than those that might be found with standard informationretrieval techniques. Learning-to-rank or machine-learned ranking is theapplication of machine learning, typically supervised, semi-supervisedor reinforcement learning, in the construction of ranking models forinformation retrieval systems. Training data consists of lists of itemswith some partial order specified between items in each list. This orderis typically induced by giving a numerical or ordinal score or a binaryjudgment (e.g. “relevant” or “not relevant”) for each item. The rankingmodel's purpose is to rank, i.e. produce a permutation of items in new,unseen lists in a way which is “similar” to rankings in the trainingdata in some sense.

As described above, methods that Formula 1 teams typically use choose aspecific mix settings map or other vehicle configuration includes theuse of spreadsheets, analysis software, database reports, andperformance graphs. Such methods tend to be laborious, time-consuming,and require a high level of user interaction and direction to producethe final, fully-optimized map. The more manual methods can be much lessefficient, and other methods still fail to identify hidden patterns inlarge quantities of data, which, if revealed, could significantly impactracing performance (lap times, speed, engine wear, etc.) on the track.

It would, therefore, be desirable to devise a system which could performcognitive analysis on the relevant data sets to identify mix and otherautomotive settings that can improve lap times and race results. Forexample, cognitive analysis may identify that although one setting mayimprove speed in turn #3 by 0.100 seconds, the data might reveal throughanalytics that using a different setting through turn #3 may reduce tireor brake temperatures, which would improve performance through turn #4by 0.200 seconds (giving a net improvement in lap time of 0.100seconds). The present invention can use a system and process to analyzeperformance and other data for selected conditions to create an optimalvehicle setting for that condition. By performing a guided interactionwith the user (e.g., the driver) to access a big data repository and acognitive system to suggest possible conditions and automotive settings,the car's settings can be fully optimized for a particular condition.For racing engine mix settings, the process guides the user using thepattern template, cognitive analysis, and interactions so that thegoals, equipment, squad and conditions are combined into collection sets(items collected into the analysis, as a factor to be used to producethe result).

Vehicle settings can be further optimized by comparing and combiningsettings from multiple conditions. Suggestion sets (items that arepresented as available for selection by the user) and collection setscan be compared and blended to modify and tune their behavior. Bycomparing and blending the automotive settings, the driver can create acomposed condition which can be published to an engine mode setting.This process of progressive filtering using a guided interrogation ofthe cognitive system enables the engine mix and other automotivesettings to be composed using a wide variety of available factors andconditions, then published as reusable constructs.

With reference now to the figures, and in particular with reference toFIG. 1, there is depicted one embodiment 10 of a computer system inwhich the present invention may be implemented to carry out the variousprocesses associated with pattern template generation and determinationof automotive settings in accordance with the present invention.Computer system 10 is a symmetric multiprocessor (SMP) system having aplurality of processors 12 a, 12 b connected to a system bus 14. Systembus 14 is further connected to and communicates with a combined memorycontroller/host bridge (MC/HB) 16 which provides an interface to systemmemory 18. System memory 18 may be a local memory device oralternatively may include a plurality of distributed memory devices,preferably dynamic random-access memory (DRAM). There may be additionalstructures in the memory hierarchy which are not depicted, such ason-board (L1) and second-level (L2) or third-level (L3) caches. Systemmemory 18 has loaded therein one or more applications in accordance withthe present invention which may perform various functions such asdisplaying a user interface and/or pattern template generation.

MC/HB 16 also has an interface to peripheral component interconnect(PCI) Express links 20 a, 20 b, 20 c. Each PCI Express (PCIe) link 20 a,20 b is connected to a respective PCIe adaptor 22 a, 22 b, and each PCIeadaptor 22 a, 22 b is connected to a respective input/output (I/O)device 24 a, 24 b. MC/HB 16 may additionally have an interface to an I/Obus 26 which is connected to a switch (I/O fabric) 28. Switch 28provides a fan-out for the I/O bus to a plurality of PCI links 20 d, 20e, 20 f. These PCI links are connected to more PCIe adaptors 22 c, 22 d,22 e which in turn support more I/O devices 24 c, 24 d, 24 e. The I/Odevices may include, without limitation, a keyboard, a graphicalpointing device (mouse), a microphone, a display device, speakers, apermanent storage device (hard disk drive) or an array of such storagedevices, an optical disk drive which receives an optical disk 25 (oneexample of a computer readable storage medium) such as a CD or DVD, anda network card. Each PCIe adaptor provides an interface between the PCIlink and the respective I/O device. MC/HB 16 provides a low latency paththrough which processors 12 a, 12 b may access PCI devices mappedanywhere within bus memory or I/O address spaces. MC/HB 16 furtherprovides a high bandwidth path to allow the PCI devices to access memory18. Switch 28 may provide peer-to-peer communications between differentendpoints and this data traffic does not need to be forwarded to MC/HB16 if it does not involve cache-coherent memory transfers. Switch 28 isshown as a separate logical component but it could be integrated intoMC/HB 16.

In this embodiment, PCI link 20 c connects MC/HB 16 to a serviceprocessor interface 30 to allow communications between I/O device 24 aand a service processor 32. Service processor 32 is connected toprocessors 12 a, 12 b via a JTAG interface 34, and uses an attentionline 36 which interrupts the operation of processors 12 a, 12 b. Serviceprocessor 32 may have its own local memory 38, and is connected toread-only memory (ROM) 40 which stores various program instructions forsystem startup. Service processor 32 may also have access to a hardwareoperator panel 42 to provide system status and diagnostic information.

In alternative embodiments computer system 10 may include modificationsof these hardware components or their interconnections, or additionalcomponents, so the depicted example should not be construed as implyingany architectural limitations with respect to the present invention. Theinvention may further be implemented in an equivalent cloud computingnetwork.

When computer system 10 is initially powered up, service processor 32uses JTAG interface 34 to interrogate the system (host) processors 12 a,12 b and MC/HB 16. After completing the interrogation, service processor32 acquires an inventory and topology for computer system 10. Serviceprocessor 32 then executes various tests such as built-in-self-tests(BISTs), basic assurance tests (BATs), and memory tests on thecomponents of computer system 10. Any error information for failuresdetected during the testing is reported by service processor 32 tooperator panel 42. If a valid configuration of system resources is stillpossible after taking out any components found to be faulty during thetesting then computer system 10 is allowed to proceed. Executable codeis loaded into memory 18 and service processor 32 releases hostprocessors 12 a, 12 b for execution of the program code, e.g., anoperating system (OS) which is used to launch applications and inparticular the vehicle settings map application of the presentinvention, results of which may be stored in a hard disk drive of thesystem (an I/O device 24). While host processors 12 a, 12 b areexecuting program code, service processor 32 may enter a mode ofmonitoring and reporting any operating parameters or errors, such as thecooling fan speed and operation, thermal sensors, power supplyregulators, and recoverable and non-recoverable errors reported by anyof processors 12 a, 12 b, memory 18, and MC/HB 16. Service processor 32may take further action based on the type of errors or definedthresholds.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Computer system 10 carries out program instructions for a process thatuses novel analysis techniques to derive a pattern template for use in ahigh-performance vehicle (such as a Formula 1 racing car). Accordingly,a program embodying the invention may include conventional aspects ofvarious engine configuration tools, and these details will becomeapparent to those skilled in the art upon reference to this disclosure.

Referring now to FIG. 2, there is depicted one implementation for theracing scenario analysis 50 which computer system 10 may carry out inorder to extract racing scenario data from an actual stint, lap, race,or collection of stints, laps, or races. Racing scenario analysis 50 canreceive racing information from a racing data repository 52. Racing datarepository 52 contains data (both historic and real-time) pertaining tovarious racing scenarios, including racing conditions, racing vehiclesettings, and racing results (final or intermediate).

Racing scenario analysis 50 can extract data from races relating to avariety of different racing conditions. In the illustrative embodiment,racing scenario analysis 50 extracts racing data relating to theenvironment, squad, equipment, and goals. While FIG. 3 shows only thesecategories for racing data, those skilled in the art will appreciatethat other categories may be extracted as well.

Goals for mix and other automotive settings can include many possiblefactors. One type of goal might have to do with higher speeds, betteracceleration or faster lap times. Other goals could also include, amongother things, more stability, less engine wear, better fuel economy,reduced brake wear, reduced tire wear, and improved cornering. Goalswill typically have performance metrics or variables associated withthem which indicate whether and to what extent a given goal has been oris being met. Goals are indicated by the racing results in racing datarepository 52

Environmental data includes data about specific track conditions. Thisincludes information about the climate during a specific racing scenario(such as rainfall amount, track temperature, and air temperature anddensity) in addition to information pertaining to the characteristics ofthe track itself (such as track geography, the degree and pitch of aspecific curve, and the clag factor). Data pertaining to the conditionof the car can be considered environmental data as well. A racingscenario could include data pertaining to the stage of race, carcondition, tire wear, or any other condition relevant to the state ofthe car.

Squad information can have several facets. In high-performance racing,squads are usually composed of the driver, pit crew, engineers, supportcrew, pit wall, factory and tire teams, and leadership. Other people orentities not listed may also be included in racing scenario squad data.

Equipment includes specific hardware and software configurations for thevehicle. A typical racing car might have a chassis, power units, kineticenergy recovery system, electronics, steering wheel, andsuspension/wing/brake/aero kits.

While these examples of racing data are fairly comprehensive, this listis not intended to be exhaustive. Racing analysis 50 can be used tocollect racing data from many separate races (e.g., hundreds orthousands), and then correlate this data with the vehicle's racesettings (engine mix setting and other configurations) to identify whichsettings are optimal for a given context. This racing data is thenconsolidated into one or more pattern templates 56 for later use by thecognitive system.

FIG. 3 shows exemplary pattern templates 56 that might be constructedvia racing analysis 50 in accordance with some implementations of thepresent invention. Each template is created using a specific set ofgoals for a specific set of racing conditions. Pattern templates areused to guide and manage the fuel mixture and other configurationsettings in a high performance/efficiency vehicle. Using atemplate-driven conversational user interface, the user can fill out asubset the data as organized on the template, resulting in a mix settingor other vehicle configuration being derived from these criteria.

Three different templates 56 a, 56 b, 56 c are provided for threedifferent goal sets. Any of these three templates could be used during asingle race depending upon the underlying racing conditions. Template 56a is for a tertiary set of goals; template 56 b is for a secondary setof goals; template 56 c is for a primary set of goals. In theseexamples, the goals for template 56 a are purely about speed andacceleration, the goals for template 56 b purely pertain to vehicleequipment wear, and the goals for template 56 c are based on otherperformance variables (e.g., better fuel efficiency).

FIG. 3 provides sample details for the racing conditions in the primarygoal template 56 a. Data pertaining environmental variables are includedin this specific pattern template, such as the weather conditions(cloudy/humid), track temperature (hot), and track geography (hilly).Other information included in the template is data pertaining to boththe state of the equipment on the vehicle (worn tires; fair carcondition) and the racing squad (full pit crew). This list is notintended to be exhaustive but merely illustrative of the possible typesof racing conditions that can be contained within the pattern template.

Primary goal template 56 a also contains data about the variouscustomizable car/automotive/vehicle settings which influence theperformance of the vehicle. One such setting is the engine mix settings,which (as discussed above) dictates the richness or leanness of theair-fuel mixture. Other automotive settings include the camber angle(−5°), shock absorber stiffness (soft), and tire contact area (large).This list is not intended to be exhaustive but merely illustrative ofthe possible types of customizable automotive configurations containedwithin the pattern template. Templates may have a default setting forcertain automotive configurations. A given setting may have multiplecandidate values.

The system is not limited to use of a single pattern template, butrather is able to use more than one pattern template. This isaccomplished by pre-defining and storing more than one template or aseries of templates, and then combining these templates to form a largertemplate. The template instance is, in effect, in a multi-dimensionalmatrix of templates. For the example, there may only be a few parts ofthe racing squad/team, so a smaller template would only include dataavailable in the garage.

The user can select a given template when creating a mix setting whichcomes from a palette of templates, preferably suggested by a cognitivesystem. These templates can be compared to one another and blendedtogether to create a composed template which can be published to anengine mode setting. For example, one template instance may produce amix setting result, and another may produce a suspension camber offset,and these would collaborate to optimize to achieve the goal, such asfaster speed through a particular corner. Combining pattern templates(both complete and incomplete) as a subset that can be substituted intothe larger template and possibly override patterns of other templates.

FIG. 4 shows generally how a template-driven user interface 60 works inthe illustrative implementation. This automated system enables thecomposition of mix and other automotive settings to be a derived from apre-defined set of steps, roles, filters, and factors. Using userinterface 60, a mix or other automotive setting is composed by leading adialog with the cognitive system by composing factors into filter sets,suggestion sets, and collection sets using a pattern template. Forcomposing mix settings to optimize the performance of automotive powerunits, the system provides a pattern of filters and conditions relevantto the car, driver, team and track. User interface 60 may be presentedas a touch screen within the race car, controlled by the driver.

The presentation of the pattern template 56 guides the cognitiveanalysis to provide suggestion and collection sets, which steps tofollow, as well as other factors related to the current step, selectedfactors, filters, templates, history, and other aspects of the state ofthe analysis. The automated system guides selections by presenting apalette of factors and filters from which to select, edit, blend,compare and compose. In the example user interface 60, the question andanswers are represented as a honeycomb of cells, navigational aids, andinformation areas and glyphs. The system allows the order of selectingand navigating among the conditions and factors to be arbitrary.

The system enables the selection of a filter 64 followed by presentationof a palette of suggested filters 66 and factors 68, where factors canbe added to a suggestion set 74 and transitioned to other suggestion orcollection sets 70 and 72 and to a staging set 76, which may be added toa result set 78 and publishing set 79. Selected items can publishcontent and editors a multi-function display 92 (shown in FIG. 5).

The automated system provides a user interface to select filters 64 andfactors 68, and add them in collection sets representing results. Filter64 can consist of a set of keywords, queries, commands, or questionssuch that the system displays a filtered list of possible values. Whenan item is selected, a filter is constructed. As an illustrativeexample, when “Tire Choice” is selected, “Hard,” “Medium,” “Soft,”“Super-Soft,” and “Ultra-Soft” would be presented as refinements of theselection. “Tire Choice” is the filter, and filters are designed usingthe same paradigm. The automated system provides suggestions such aswhich filters to select (i.e. suggested filter 66) and which factors 68to select from using a pattern stored in the pattern template 56. Factorset 70 represents a collection of factors, while filter set 72represents a collection of filters.

Selecting a filter 64 displays a set of other suggested filters 66, plusfactors 68 that can be applied to the question package. Factor 68describes a particular contributor to a condition. A given factor 68 canhave many attributes, such as values and ranges, which can be edited asproperties to tune its relevance. Suggested filter 66 and factor 68 showa set of selections around a filter.

Suggestion set 74 is a set of selected items held in a staging area.When a user selects an item on the screen, the properties of the itemare displayed in the inspector. In addition a set of related objects arecognitively suggested and presented to the user. When a user findssomething that they want to include in the analytics, they add it to acollection set in the stage. When a topic is selected, possible valuesare presented. These are then added to the center staging area.

In this illustrative embodiment, when a cell is selected, the propertiesare presented, and related items are presented on the screen. There is astage in the middle (staging set 76) where the selections are composed,and a palette of selections presented which can be explored or selected.The production ring is the stage in the center (staging set 76) and iswhere the selections are collected. When any item is selected, theselection area changes to show the new selections, and also enables theuser to drill down. The general pattern of initial filters is in theoutermost hex of the stage, and these can be refined through furtherfilters and selections. Going through this cycle is one pattern type ofthe template, but this is not to be construed in a limiting sense, asthere can be others.

Result set 78 is a set of selected items collected for publishing or useas a composed unit. Result set 78 contains all of the mix and otherautomotive settings to be published. These may not necessarily be shown,may not be mutable, and might only be automatically published. Resultset 78 is in a format where the mix settings are actually determined.Publishing set 79 is a collection of items (i.e. results) that have beenpublished.

The pattern template 56 is a collection of possible values to seed theanalysis and composition on a template-driven user interface 60. FIG. 5is an illustration of a specific pattern template 80 loaded onto thetemplate-driven conversational user interface. It provides for a way tostore and recall a specific question and answer domain, and applies theprocess to a particular domain. The specific pattern template 80 guidesthe user through a set of complex choices using cognitive search andpattern matching to provide relevant topics for which data can beobtained and question and answer can commence. If a process is followedthat fills out a subset the data as organized on this specific patterntemplate 80, then a mix or other automotive configuration can be derivedfrom these criteria. In addition, once a path has been followed, it canbe saved for the future, where different answers or paths can beexplored. A more complex template captures a set of sessions as amulti-dimensional structure of options followed or available.

For composing for racing engine mix settings the pattern template iscomprised of filters, factors and conditions including but not limitedto a subset of environmental conditions 82, squad variables 84,equipment variables 86, and goals 88. The illustrative embodiment inFIG. 5 shows that the equipment variable “Tires Primed” has beenselected. This pulls up the window on the right (multi-function display92), as well as pulling up the flattened hexes representing factorsuggestions 94 a-c and corresponding factor choices 96 a-c. Selecting“Tires Primes” displays other factors 94 a-c which are somehow relatedor associated (ex. “fuel boost”, “torque value”, “fuel limit”), andsuggested values for those factors. A user can select a hex around theoctagon, which is then added to the automotive settings 98. Selecting“Tires Primed” can also bring up the drill down hexes 100 inside“Equipment” and “Conditions.”

Once a sufficient number of automotive settings has been added, a usercan select results 102, which will show the user the results (i.e. allof the selected conditions, factors, and settings) of the particularselections which have been made. A user also has the choice to selectpublication 102, which will publish the results to another computersystem, terminal, or user interface.

The present invention may be further understood with reference to thechart of FIG. 6 which illustrates the logical flow for a patterntemplate generation process 110. Process 110 begins when a computingdevice such as computer system 10 receives racing scenarios 112. Thescenarios are associated with optimal results 114. This association maybe based on a variety of criteria, such as performance scores orparticular goals involved (such as better acceleration or less enginewear). The criteria may be adjusted to ensure an adequate sampling ofthe scenarios. Use of multiple scenarios allows the cognitive system tostatistically correlate particular racing data with optimal racingsettings and configurations 114. In other words, if one particularautomotive setting or configuration is present for a particular contextin an overwhelming number of optimal results, that configuration isdesignated as a primary suggestion in the template by the cognitivesystem. The exact statistical parameters defining a positive correlationare up to the designer of the scenario analysis logic.

The computer system can take the optimal scenarios and theircorresponding automotive settings and use them to generate the templates116, which are then saved for later deployment 118. Pattern templatesmay be updated or replaced over time by including more recent racingscenarios and/or excluding older racing scenarios from the analysis.This continuous updating of the templates helps keep the cognitive Q&Arelevant to current racing conditions.

In one possible implementation a cognitive system can take as input aselection made by the user, which would then compose a Watson Retrieveand Rank query to provide the other related suggestions at variousconfidence levels (e.g., 50%, 90%). That is, making a selection willshow more suggestions to refine or change the parameters. Adding asuggestion to the stage would include the selection's parameter valuesin the analytics.

FIG. 7 illustrates how the templates may subsequently be used inaccordance with the present invention. This settings composition process120 begins with the computer system (e.g., cognitive analysis) receivingvarious racing conditions 122 for the current race containing data aboutthe environment, squad, equipment, and goals. The cognitive system thenuses the racing scenario data to create a unique pattern template whichis specially customized to the same or similar set of racing data 124.That template is then applied to exercise control over the display andnavigation of the user interface 126. Upon the user filling in (eitherpartially or completely) the choices provided in the pattern template asdisplayed on the user interface 128, a mix or other automotive settingcan be arrived at 130. In effect, the value ranges, data repositories,visualizations, result descriptors, and other factors selected whilenavigating the selection space guided by the template becomes a usefulanalytic resource for the user, such that the mix or other automotivesettings needed to program the car are determined 130 after sufficientuser input. After these settings have been presented to the user, theuser then has the choice to publish these automotive settings 132 toanother system. The user interface provides for publishing, storing andsharing results. Publishing 132 can be to a remote system, a connectedor intelligent device such as a steering wheel, to a screen, orelsewhere. For Formula 1 mix settings, the results could be published tothe steering wheel, engine control unit, as well as engineering and raceworkstations.

The present invention accordingly possesses many advantages overconventional automotive configuration strategies. It is easily adaptedto any set of racing conditions and any UI format. Leveraging thetactics and strategies used by cognitive computing results in a superiorautomotive configuration setup as compared to setups which morerudimentary or manual methods would most likely produce. Bydeconstructing and analyzing statistics of the strategies used in a setof racing scenarios, and combining them with optimal automotivesettings, the user is able to deploy optimal automotive settings in atime-relevant manner. The invention not only makes the optimal choicesfor automotive settings, but additionally makes it easier for a user tocustomize and find the optimal settings, thus providing a more usefulanalytic resource when compared to spreadsheets or traditional datavisualizations.

Although the invention has been described with reference to specificembodiments, this description is not meant to be construed in a limitingsense. Various modifications of the disclosed embodiments, as well asalternative embodiments of the invention, will become apparent topersons skilled in the art upon reference to the description of theinvention. For example, while the invention has been described in thecontext of Formula 1 racing, it is of course applicable to other typesof car racing, as well as other vehicles such as motorcycles. Theinvention further has applicability in non-race settings, such asoptimizing fuel consumption, equipment wear, or speed in fleet vehicles.It is therefore contemplated that such modifications can be made withoutdeparting from the spirit or scope of the present invention as definedin the appended claims.

What is claimed is:
 1. A method of developing a racing strategy forvehicle settings comprising: receiving a plurality of racing scenariosfrom a racing data repository wherein each racing scenario includesracing conditions, racing vehicle settings, and racing results, byexecuting first instructions in a computer system; analyzing the racingscenarios to associate specific racing vehicle settings with certainracing conditions and optimal racing results, by executing secondinstructions in the computer system; and creating a pattern template forone or more racing goals based on the specific racing vehicle settingsand certain racing conditions, by executing third instructions in thecomputer system.
 2. The method of claim 1 wherein the racing goals areselected from the group consisting of faster lap time, betteracceleration, less vehicle wear, better fuel economy, more stability,and improved cornering.
 3. The method of claim 1 wherein the racingconditions include at least squad information, equipment information,and environmental information.
 4. The method of claim 1 wherein thepattern template includes a set of filters.
 5. The method of claim 1wherein the pattern template includes a set of suggestions of the racingvehicle settings.
 6. The method of claim 1 wherein the pattern templateincludes at least one factor having multiple candidate settings.
 7. Themethod of claim 1 wherein the settings include an engine mix settingspecifying an air-to-fuel ratio.
 8. A computer system comprising: one ormore processors which process program instructions; a memory deviceconnected to said one or more processors; and program instructionsresiding in said memory device for developing a racing strategy forvehicle settings by receiving a plurality of racing scenarios from aracing data repository wherein each racing scenario includes racingconditions, racing vehicle settings, and racing results, analyzing theracing scenarios to associate specific racing vehicle settings withcertain racing conditions and optimal racing results, and creating apattern template for one or more racing goals based on the specificracing vehicle settings and certain racing conditions.
 9. The computersystem of claim 8 wherein the racing goals are selected from the groupconsisting of faster lap time, better acceleration, less vehicle wear,better fuel economy, more stability, and improved cornering.
 10. Thecomputer system of claim 8 wherein the racing conditions at leastincludes squad information, equipment information, and environmentalinformation.
 11. The computer system of claim 8 wherein the patterntemplate includes a set of filters.
 12. The computer system of claim 8wherein the pattern template includes a set of suggestions of the racingvehicle settings.
 13. The computer system of claim 8 wherein the patterntemplate includes at least one factor having multiple candidatesettings.
 14. The computer system of claim 8 wherein the settingsinclude an engine mix setting specifying an air-to-fuel ratio.
 15. Acomputer program product comprising: a computer readable storage medium;and program instructions residing in said storage medium for developinga racing strategy for vehicle settings by receiving a plurality ofracing scenarios from a racing data repository wherein each racingscenario includes racing conditions, racing vehicle settings, and racingresults, analyzing the racing scenarios to associate specific racingvehicle settings with certain racing conditions and optimal racingresults, and creating a pattern template for one or more racing goalsbased on the specific racing vehicle settings and certain racingconditions.
 16. The computer program product of claim 15 wherein theracing goals are selected from the group consisting of faster lap time,better acceleration, less vehicle wear, better fuel economy, morestability, and improved cornering.
 17. The computer program product ofclaim 15 wherein the racing conditions at least includes squadinformation, equipment information, and environmental information. 18.The computer program product of claim 15 wherein the pattern templateincludes a set of filters.
 19. The computer program product of claim 15wherein the pattern template includes a set of suggestions of the racingvehicle settings.
 20. The computer program product of claim 15 whereinthe pattern template includes at least one factor having multiplecandidate settings.